Search Results for author:
Found 22 papers, 10 papers with code
PDALN: Progressive Domain Adaptation over a Pre-trained Model for Low-Resource Cross-Domain Named Entity Recognition
Cross-domain Named Entity Recognition (NER) transfers the NER knowledge from high-resource domains to the low-resource target domain.
KALAHash: Knowledge-Anchored Low-Resource Adaptation for Deep Hashing
Deep hashing has been widely used for large-scale approximate nearest neighbor search due to its storage and search efficiency.
Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs
In this paper, we propose P2TAG, a framework designed for few-shot node classification on TAGs with graph pre-training and prompting.
TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
We introduce TableLLM, a robust large language model (LLM) with 13 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embedded within documents or spreadsheets, catering to real-world office scenarios.
Reconstruct before Query: Continual Missing Modality Learning with Decomposed Prompt Collaboration
Meanwhile, our RebQ leverages extensive multi-modal knowledge from pre-trained LMMs to reconstruct the data of missing modality.
Why are hyperbolic neural networks effective? A study on hierarchical representation capability
Inspired by the analysis results, we propose several pre-training strategies to enhance HRC and improve the performance of downstream tasks, further validating the reliability of the analysis.
NEUCORE: Neural Concept Reasoning for Composed Image Retrieval
Specifically, considering that text modifier may refer to semantic concepts not existing in the reference image and requiring to be added into the target image, we learn the multi-modal concept alignment between the text modifier and the concatenation of reference and target images, under multiple-instance learning framework with image and sentence level weak supervision.
Less is More: Toward Zero-Shot Local Scene Graph Generation via Foundation Models
To fill this gap, we present a new task called Local Scene Graph Generation.
GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model
Currently, the reduction in the parameter scale of large-scale pre-trained language models (PLMs) through knowledge distillation has greatly facilitated their widespread deployment on various devices.
Are Intermediate Layers and Labels Really Necessary? A General Language Model Distillation Method
To address these problems, we propose a general language model distillation (GLMD) method that performs two-stage word prediction distillation and vocabulary compression, which is simple and surprisingly shows extremely strong performance.
Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Driving
However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems.
Automatic Analysis of Human Body Representations in Western Art
The way the human body is depicted in classical and modern paintings is relevant for art historical analyses.
Aesthetic Visual Question Answering of Photographs
In this paper, we propose a new task of aesthetic language assessment: aesthetic visual question and answering (AVQA) of images.
Attribute Controllable Beautiful Caucasian Face Generation by Aesthetics Driven Reinforcement Learning
In recent years, image generation has made great strides in improving the quality of images, producing high-fidelity ones.
Hyperbolic Hierarchical Knowledge Graph Embeddings for Link Prediction in Low Dimensions
Knowledge graph embeddings (KGE) have been validated as powerful methods for inferring missing links in knowledge graphs (KGs) that they typically map entities into Euclidean space and treat relations as transformations of entities.
Coherence-Based Distributed Document Representation Learning for Scientific Documents
In this paper, we propose a coupled text pair embedding (CTPE) model to learn the representation of scientific documents, which maintains the coherence of the document with coupled text pairs formed by segmenting the document.
JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets
In this paper, we propose an inter-cascade generative adversarial network, namely JAS-GAN, to segment the unbalanced atrial targets from LGE CMR images automatically and accurately in an end-to-end way.
Rescuing Deep Hashing from Dead Bits Problem
The proposed gradient amplifier and error-aware quantization loss are compatible with a variety of deep hashing methods.
A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation
Paper-reviewer recommendation task is of significant academic importance for conference chairs and journal editors.
Discriminative Consistent Domain Generation for Semi-supervised Learning
Based on the generated discriminative consistent domain, we can use the unlabeled data to learn the task model along with the labeled data via a consistent image generation.
Direct Quantification for Coronary Artery Stenosis Using Multiview Learning
The proposed DMQCA model consists of a multiview module with two attention mechanisms, a key-frame module, and a regression module, to achieve direct accurate multiple-index estimation.
Direct detection of pixel-level myocardial infarction areas via a deep-learning algorithm
Accurate detection of the myocardial infarction (MI) area is crucial for early diagnosis planning and follow-up management.
